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20162022
most citedTask Offloading for Large-Scale Asynchronous Mobile Edge Computing: An Index Policy Approach

24 citations · 193 across the 52 of their papers we have counts for

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28 papers · 1 filter

eess.SP2022

Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks

Dawei Gao, Qinghua Guo, Guisheng Liao +4

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in…

eess.SP2022

Significant Low-dimensional Spectral-temporal Features for Seizure Detection

Xucun Yan, Dongping Yang, Zihuai Lin +1

Seizure onset detection in electroencephalography (EEG) signals is a challenging task due to the non-stereotyped seizure activities as well as their stochastic and non-stationary c…

eess.SP2021

Bayesian-based Symbol Detector for Orthogonal Time Frequency Space Modulation Systems

Xinwei Qu, Alva Kosasih, Wibowo Hardjawana +2

Recently, the orthogonal time frequency space (OTFS) modulation is proposed for 6G wireless system to deal with high Doppler spread. The high Doppler spread happens when the transm…

eess.SP2021

Improving Cell-Free Massive MIMO Detection Performance via Expectation Propagation

Alva Kosasih, Vera Miloslavskaya, Wibowo Hardjawana +2

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) technology plays a prominent role in the beyond fifth-generation (5G) networks. However, designing a high performance…

eess.SP2020

Performance Analysis and Optimization of NOMA with HARQ for Short Packet Communications in Massive IoT

Fatemeh Ghanami, Ghosheh Abed Hodtani, Branka Vucetic +1

In this paper, we consider the massive non-orthogonal multiple access (NOMA) with hybrid automatic repeat request (HARQ) for short packet communications. To reduce the latency, eac…

eess.SP2020

Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation

Zhouyou Gu, Changyang She, Wibowo Hardjawana +4

In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL) algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with time-se…